parser.add_argument("--gamma", help="Learning rate decay factor", type=float, default=0.9)
parser.add_argument("--epochs", help="Total number of epochs to train", type=int, default=250)
parser.add_argument("--len_epoch", help="Number of iteration in each epoch", type=int, default=100)
parser.add_argument("--test_freq", help="Number of epochs between each test stage", type=int, default=1)
parser.add_argument("--train_batch_size", help="Batch size in the training stage", type=int, default=4)
parser.add_argument("--test_batch_size", help="Batch size in the testing stage", type=int, default=4)
args = parser.parse_args()
print('[%s] Arguments: ' % (datetime.datetime.now()))
print('[%s] %s' % (datetime.datetime.now(), args))
args.input_size = [int(item) for item in args.input_size.split(',')]
"""
Fix seed (for reproducibility)
"""
set_seed(args.seed)
"""
Setup logging directory
"""
print('[%s] Setting up log directories' % (datetime.datetime.now()))
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
"""
Load the data
"""
df = json.load(open(args.data_file))
input_shape = (1, args.input_size[0], args.input_size[1])
print('[%s] Loading data' % (datetime.datetime.now()))
augmenter = Compose(
"-c",
help="Path to the configuration file",
type=str,
default='clem1.yaml')
parser.add_argument(
"--clean-up",
help="Boolean flag that specifies cleaning of the checkpoints",
action='store_true',
default=False)
args = parser.parse_args()
with open(args.config) as file:
params = parse_params(yaml.load(file, Loader=yaml.FullLoader))
"""
Fix seed (for reproducibility)
"""
set_seed(params['seed'])
"""
Load the data
"""
print_frm('Loading data')
input_shape = (1, *(params['input_size']))
split_src = params['src']['train_val_split']
split_tar = params['tar']['train_val_split']
transform = Compose([
Rotate90(),
Flip(prob=0.5, dim=0),
Flip(prob=0.5, dim=1),
ContrastAdjust(adj=0.1),
AddNoise(sigma_max=0.05)
])
print_frm('Train data...')
